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Collaborative filtering for people to people recommendation in social networks

Citation

Cai, X and Bain, M and Krzywicki, A and Wobcke, W and Kim, YS and Compton, P and Mahidadia, A, Collaborative filtering for people to people recommendation in social networks, Lecture Notes in Computer Science Volume 6464: AI 2010, 7-10 December 2010, Adelaide, Australia, pp. 476-485. ISBN 978-3-642-17431-5 (2010) [Refereed Conference Paper]

Copyright Statement

Copyright 2010 Springer

DOI: doi:10.1007/978-3-642-17432-2_48

Abstract

Predicting people other people may like has recently become an important task in many online social networks. Traditional collaborative filtering approaches are popular in recommender systems to effectively predict user preferences for items. However, in online social networks people have a dual role as both "users" and "items", e.g., both initiating and receiving contacts. Here the assumption of active users and passive items in traditional collaborative filtering is inapplicable. In this paper we propose a model that fully captures the bilateral role of user interactions within a social network and formulate collaborative filtering methods to enable people to people recommendation. In this model users can be similar to other users in two ways either having similar "taste" for the users they contact, or having similar "attractiveness" for the users who contact them.We develop SocialCollab, a novel neighbourbased collaborative filtering algorithm to predict, for a given user, other users they may like to contact, based on user similarity in terms of both attractiveness and taste. In social networks this goes beyond traditional, merely taste-based, collaborative filtering for item selection. Evaluation of the proposed recommender system on datasets from a commercial online social network show improvements over traditional collaborative filtering.

Item Details

Item Type:Refereed Conference Paper
Keywords:recommender systems, social network analysis
Research Division:Information and Computing Sciences
Research Group:Artificial Intelligence and Image Processing
Research Field:Artificial Intelligence and Image Processing not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Application Software Packages (excl. Computer Games)
Author:Kim, YS (Dr Yang Kim)
ID Code:94648
Year Published:2010
Deposited By:Computing and Information Systems
Deposited On:2014-09-15
Last Modified:2014-10-20
Downloads:0

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